Abstract

With the new advancements in Internet of Things (IoT) and its applications in different sectors, such as the industrial sector, by connecting billions of devices and instruments, IoT has evolved as a new paradigm known as the Industrial Internet of Things (IIoT). Nonetheless, its benefits and applications have been approved in different areas, but there are possibilities for various cyberattacks because of its extensive connectivity and diverse nature. Such attacks result in financial loss and data breaches, which urge a consequential need to secure IIoT infrastructure. To combat the threats in the IIoT environment, we proposed a deep-learning SDN-enabled intelligent framework. A hybrid classifier is used for threat detection purposes, i.e., Cu-LSTMGRU + Cu-BLSTM. The proposed model achieved a better detection accuracy with low false-positive rate. We have conducted 10-fold cross-validation to show the unbiasdness of the results. The proposed scheme results are compared with Cu-DNNLSTM and Cu-DNNGRU classifiers, which were tested and trained on the same dataset. We have further compared the proposed model with other existing standard classifiers for a thorough performance evaluation. Results achieved by our proposed scheme are impressive with respect to speed efficiency, F1 score, accuracy, precision, and other evaluation metrics.

Highlights

  • The Industrial Internet of Things (IIoT) connects physical machines, sensors, and devices with the Internet

  • The results showed that false positive rate (FPR) is 0% with 97% CPU utilization for captured traffic of 10,000 packets at the rate of one second

  • Using the standard evaluation metrics such as precision, recall, accuracy, and F1score, we evaluated the performance of the proposed architecture

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Summary

Introduction

The Industrial Internet of Things (IIoT) connects physical machines, sensors, and devices with the Internet. It uses various software to perform deep analytics and transform vast amounts of data into powerful insights and intelligence [1]. This term highlights the IoT and its applications in sectors such as the Industrial sector, with strong attention on machine-to-machine (M2M) communication, machine learning (ML), and big data. The architecture of SDN comprises a data plane, control plane, and application plane with their APIs, i.e., northbound API and southbound API. Communication with the switch fabric, network virtualization protocols, and the integration of a distributed computing network are all functions of southbound APIs. According to SDNs architecture, we have a control plane isolated from the application and data plane. For different SDN controllers, the architecture and design for most of them are the same; they differ in functionalities

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